What we offer
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Work in a Scandinavian culture built on logic, trust, and open dialogue.
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Health and sports benefits tailored to your needs.
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Speed Coaching – short sessions designed to provide new perspectives and solutions.
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Sports training, an internal team, and opportunities to take part in competitions.
Key responsibilities:
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AI/ML Epic ownership and backlog prioritization - Translating business needs and AI/ML opportunities into Epics and Features with clearly defined value, scope, and success criteria. Driving them from discovery through delivery to adoption. Prioritizing initiatives based on business value, strategic fit, feasibility, dependencies, and capacity, and maintaining a transparent AI/ML backlog
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Interface between the business and AI/ML teams - Acting as the primary point of contact for business stakeholders. Aligning on objectives, scope, priorities, and expected outcomes, managing expectations, and communicating progress, dependencies, risks, and trade-offs
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Delivery and business value realization - Working with Tech Leads, Data Scientists, and ML Engineers to shape feasible solutions. Monitoring progress, risks, and blockers, and following up on adoption and measurable business value, not only technical delivery
What you need to join us:
Must have:
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Senior experience as a backlog owner (e.g., Product Owner, Epic Owner, Product Manager) - prioritization, delivery coordination, and feature-level backlog management in Agile/DevOps environments
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Hands-on experience driving AI/ML initiatives, including translating business needs into Epics and Features
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Experience with Generative AI and enterprise AI platforms (e.g., Databricks)
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Understanding of the AI/ML solution lifecycle - from experimentation through productionization to adoption
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Fluent English,
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based in Prague (Czech Republic), and willing to work in a hybrid model (1/week in office)
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Eligibility to work in the Czech Republic (citizenship, residence permit, visa, etc.) and Czech tax residency
Nice to have:
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Experience in data and platform environments and familiarity with their concepts (e.g., Databricks, Data Products)
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Experience defining success criteria and measuring the adoption and business value of delivered AI/ML solutions
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